Demographics for all included participants.
| Demographics | ||||
| Summary | ||||
| N | Age (years) | Education (years) | Sex (M/F/O) | EHI |
|---|---|---|---|---|
| 844 | 29.08 (6.03) | 14.38 (2.48) | 441/391/12 | 5.09 (79.37) |
| Race | n |
|---|---|
| White | 606 |
| Black or African American | 82 |
| Multiple | 76 |
| Asian | 69 |
| American Indian or Alaska Native | 5 |
| Native Hawaiian or Other Pacific Islander | 3 |
| Other | 3 |
| Hispanic ethnicity | n |
|---|---|
| No | 744 |
| Yes | 100 |
Demographics for included participants, by handedness group (EHI bins).
| Handedness | N | Age (years) | Education (years) | Sex (M/F/O) | EHI |
|---|---|---|---|---|---|
| Left | 331 | 28.84 (6.1) | 14.45 (2.39) | 170/157/4 | -81.61 (19.27) |
| Mixed | 135 | 28.83 (6.17) | 14.58 (2.6) | 77/56/2 | -8.89 (26.49) |
| Right | 378 | 29.38 (5.93) | 14.24 (2.5) | 194/178/6 | 86.01 (16.61) |
| Left: (EHI <= -40) | Mixed: (-40 < EHI < 40) | Right: (EHI >= 40) | |||||
Do we find an interaction of field x level x handedness, when
handedness is binned as left (EHI < -40) or right (EHI > +40)?
Summary. For reaction time, we find
the critical interaction in the predicted direction (11.67ms, 95%CI
[0.65, 22.69], p = .019, one-sided). I haven’t run accuracy yet, but the
effect will be close to zero, opposite the predicted direction (the
point estimates are 1.76 for righties, 1.96 for lefties).
Error bars show 95% CI.
Reaction time is modeled as a linear effect of field, level, and
handedness, using data from every target-present trial with a “go”
response:
lmer( rt ~ field*level*handedness + (1 | subject) )
| Field by level by handedness interaction (RT) | |||||||
| ANOVA: compare models with vs. without interaction term | |||||||
| npar | AIC | BIC | logLik | deviance | Chisq | Df | p.value1 |
|---|---|---|---|---|---|---|---|
| 9 | 1,166,282.858 | 1,166,367.139 | −583,132.429 | 1,166,264.858 | - | - | - |
| 10 | 1,166,280.551 | 1,166,374.197 | −583,130.276 | 1,166,260.551 | 4.307 | 1 | .038 |
| 1 F-test (Two-sided?) | |||||||
| Field by level interaction (RT) | |||||
| Omnibus F-test | |||||
| term | df | sumsq | meansq | statistic | p.value |
|---|---|---|---|---|---|
| field | 1 | 1,664,691.722 | 1,664,691.722 | 23.612 | <.0001 |
| level | 1 | 9,626,122.373 | 9,626,122.373 | 136.54 | <.0001 |
| handedness | 1 | 10,185,712.46 | 10,185,712.46 | 144.477 | <.0001 |
| field:level | 1 | 2,730,949.837 | 2,730,949.837 | 38.737 | <.0001 |
| field:handedness | 1 | 1,505,316.949 | 1,505,316.949 | 21.352 | <.0001 |
| level:handedness | 1 | 12,247.994 | 12,247.994 | 0.174 | .677 |
| field:level:handedness | 1 | 127,954.685 | 127,954.685 | 1.815 | .178 |
| Residuals | 86,205 | 6,077,502,195.866 | 70,500.576 | - | - |
| Field by level by handedness interaction (RT) | |||||||||
| Compare effect estimate to zero with emmeans() | |||||||||
| field_consec | level_consec | handedness_consec | estimate1 | SE | df2 | asymp.LCL3 | asymp.UCL3 | z.ratio | p.value4 |
|---|---|---|---|---|---|---|---|---|---|
| LVF - RVF | Local - Global | Right - Left | 11.666 | 5.622 | Inf | 0.648 | 22.685 | 2.075 | .038 |
| 1 A positive number means LVF global bias is stronger in right handers (as predicted by AAH) | |||||||||
| 2 Z-approximation | |||||||||
| 3 Confidence level: 95% | |||||||||
| 4 Two-sided | |||||||||
| LVF Global bias by handedness bin (RT) | |||||||||
| field_consec | level_consec | handedness | estimate1 | SE | df2 | asymp.LCL3 | asymp.UCL3 | z.ratio | p.value4 |
|---|---|---|---|---|---|---|---|---|---|
| LVF - RVF | Local - Global | Left | 15.641 | 4.096 | Inf | 7.613 | 23.669 | 3.819 | .0001 |
| LVF - RVF | Local - Global | Mixed | 21.658 | 6.414 | Inf | 9.087 | 34.228 | 3.377 | .0007 |
| LVF - RVF | Local - Global | Right | 27.307 | 3.829 | Inf | 19.802 | 34.812 | 7.131 | <.0001 |
| 1 A positive number means global bias (faster RT for global) | |||||||||
| 2 Z-approximation | |||||||||
| 3 Confidence level: 95% | |||||||||
| 4 Two-sided, uncorrected | |||||||||
In progress.
Do we find an interaction of field x level x handedness (continuous EHI score)?
Summary. For reaction time, we find the critical interaction in the predicted direction (.067ms per EHI unit, or 14.82ms for EHI = -100, 28.14ms for EHI = +100. I haven’t figured out how to get confidence intervals around this estimate yet, but the one-sided p-value for an ANOVA model comparison is .020. I haven’t run the accuracy stats yet, but the subject-level scatter plot looks very flat.
Model RT as a linear effect of field, level, and EHI (continuous):
rt_model_ehi <- lmer( rt ~ field*level*ehi + (1 | subject) )
| Field by level by ehi interaction (RT) | |||||||
| ANOVA: compare models with vs. without interaction term | |||||||
| npar | AIC | BIC | logLik | deviance | Chisq | Df | p.value1 |
|---|---|---|---|---|---|---|---|
| 9 | 1,387,640.958 | 1,387,726.807 | −693,811.479 | 1,387,622.958 | - | - | - |
| 10 | 1,387,638.71 | 1,387,734.097 | −693,809.355 | 1,387,618.71 | 4.248 | 1 | .039 |
| 1 F-test (two-sided?) | |||||||
| Estimated global bias by field, for EHI of -100 | |||||
| contrast | estimate1 | SE | df | z.ratio | p.value |
|---|---|---|---|---|---|
| (LVF Local ehi-100) - (LVF Global ehi-100) | 29.412 | 3.009 | Inf | 9.776 | <.0001 |
| (RVF Local ehi-100) - (RVF Global ehi-100) | 14.59 | 3.016 | Inf | 4.837 | <.0001 |
| 1 Estimated global bias (ms) | |||||
| Estimated LVF Global Bias for EHI of -100 |
| LVF_global_bias |
|---|
| 14.822 |
| Estimated global bias by field, for EHI of +100 | |||||
| contrast | estimate1 | SE | df | z.ratio | p.value |
|---|---|---|---|---|---|
| LVF Local ehi100 - LVF Global ehi100 | 36.074 | 2.824 | Inf | 12.775 | <.0001 |
| RVF Local ehi100 - RVF Global ehi100 | 7.93 | 2.83 | Inf | 2.802 | .026 |
| 1 Estimated global bias (ms) | |||||
| Estimated LVF Global Bias for EHI of +100 |
| LVF_global_bias |
|---|
| 28.144 |
\[
28.144 - 14.822 = 13.322ms \\
13.322/200 = 0.067ms / EHI unit
\] Each unit change in EHI (-100:100) corresponds to a
0.067ms difference in LVF global bias. This is the
slope estimate given by the summary function:
summary(rt_model_ehi)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ field:level:ehi + field:level + field:ehi + level:ehi +
## field + level + ehi + (1 | subject)
## Data: aah_for_rt_ehi_model
##
## REML criterion at convergence: 1387626.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.879612 -0.590631 -0.167132 0.363313 7.653749
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 28269.4 168.135
## Residual 42128.7 205.253
## Number of obs: 102615, groups: subject, 844
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 680.1697334 5.9429525 114.44980
## fieldRVF -2.6755865 1.8307862 -1.46144
## levelGlobal -32.7431087 1.8162558 -18.02781
## ehi 0.2190687 0.0747656 2.93007
## fieldRVF:levelGlobal 21.4828945 2.5694569 8.36087
## fieldRVF:ehi -0.1228165 0.0230212 -5.33493
## levelGlobal:ehi -0.0333102 0.0228331 -1.45886
## fieldRVF:levelGlobal:ehi 0.0666075 0.0323174 2.06104
##
## Correlation of Fixed Effects:
## (Intr) fldRVF lvlGlb ehi flRVF:G flRVF: lvlGl:
## fieldRVF -0.155
## levelGlobal -0.156 0.506
## ehi -0.064 0.010 0.010
## fldRVF:lvlG 0.110 -0.713 -0.706 -0.007
## fieldRVF:eh 0.010 -0.067 -0.033 -0.154 0.047
## levelGlbl:h 0.010 -0.033 -0.065 -0.156 0.046 0.506
## fldRVF:lvG: -0.007 0.047 0.046 0.110 -0.065 -0.712 -0.706
Test for a simple correlation between each subject’s EHI and LVF
global bias.
| Subject-level correlation: linear model | ||||
| term | estimate | std.error | statistic | p.value1 |
|---|---|---|---|---|
| (Intercept) | 16.696 | 2.454 | 6.804 | <.0001 |
| ehi | 0.042 | 0.031 | 1.374 | .17 |
| 1 Two-sided | ||||
| Subject-level correlation: Spearman's rho | ||||
| rho | statistic | p.value1 | method | alternative |
|---|---|---|---|---|
| 0.041 | 96,127,760.619 | .119 | Spearman's rank correlation rho | greater |
| 1 One-sided | ||||
In progress. Model accuracy as a binomial
effect of field, level, and EHI (continuous):
acc_ehi_model <- glmer( rt ~ field*level*ehi + (1 | subject), family = "binomial" )
Test for a simple correlation between each subject’s EHI and LVF
global bias.
| Subject-level correlation: linear model | ||||
| term | estimate | std.error | statistic | p.value1 |
|---|---|---|---|---|
| (Intercept) | 1.987 | 0.311 | 6.381 | <.0001 |
| ehi | 0.002 | 0.004 | 0.448 | .655 |
| 1 Two-sided | ||||
| Subject-level correlation: Spearman's rho | ||||
| rho | statistic | p.value1 | method | alternative |
|---|---|---|---|---|
| 0.036 | 96,571,310.453 | .147 | Spearman's rank correlation rho | greater |
| 1 One-sided | ||||